New Strategy From the PRISON (http://www.lifl.fr/IPD/ipd.frame.html)

axelrod/strategies/_strategies.py

@@ -59,6 +59,7 @@
     TitForTat, TitFor2Tats, TwoTitsForTat, Bully, SneakyTitForTat,
     SuspiciousTitForTat, AntiTitForTat, HardTitForTat, HardTitFor2Tats,
     OmegaTFT, Gradual, ContriteTitForTat, SlowTitForTwoTats, AdaptiveTitForTat)
+from .worseandworse import WorseAndWorse
 
 
 # Note: Meta* strategies are handled in .__init__.py
@@ -195,12 +196,13 @@
     Willing,
     WinShiftLoseStay,
     WinStayLoseShift,
+    WorseAndWorse,
     ZDExtort2,
     ZDExtort2v2,
     ZDExtort4,
     ZDGTFT2,
     ZDGen2,
     ZDSet2,
     e,
-    
+
 ]

axelrod/strategies/worseandworse.py

@@ -0,0 +1,35 @@
+from axelrod import Actions, Player
+from random import randint
+
+class WorseAndWorse (Player):
+    """
+    Defects with probability of 'current turn / total no. of turns'. Therefore
+    it is more and more likely to defect as the round goes on.
+
+    Names:
+        - worse_and_worse: [PRISON1998]
+
+    """
+
+    name = 'Worse and worse'
+    classifier = {
+        'memory_depth': float('inf'),
+        'stochastic': True,
+        'makes_use_of': set(['length']),
+        'long_run_time': False,
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def strategy(self, opponent):
+        current_round = len(self.history) + 1
+        expected_length = self.match_attributes['length']
+        try:
+            if randint(0, expected_length) < (current_round):
+                return Actions.D
+            return Actions.C
+        except:
+            if randint(0, 200) < (current_round):
+                return Actions.D
+            return Actions.C

axelrod/tests/unit/test_worse_and_worse.py

@@ -0,0 +1,33 @@
+"""Test for the Worse and Worse strategy."""
+
+import axelrod
+
+from .test_player import TestPlayer
+
+C, D = axelrod.Actions.C, axelrod.Actions.D
+
+
+class TestWorseAndWorse(TestPlayer):
+
+    name = "Worse and worse"
+    player = axelrod.WorseAndWorse
+    expected_classifier = {
+        'memory_depth': float('inf'),
+        'stochastic': True,
+        'makes_use_of': set(['length']),
+        'long_run_time': False,
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def test_strategy(self):
+        """
+        Test that the stratergy chooses to defect according to the correct
+        probability.
+        """
+        self.responses_test([], [], [C, C, D, D, D], random_seed=1,
+        tournament_length=5)
+
+        self.responses_test([], [], [D, D, D, D, D], random_seed=2,
+        tournament_length=5)

docs/reference/all_strategies.rst

@@ -60,6 +60,9 @@ Here are the docstrings of all the strategies in the library.
 .. automodule:: axelrod.strategies.gobymajority
    :members:
    :undoc-members:
+.. automodule:: axelrod.strategies.gradualkiller
+   :members:
+   :undoc-members:
 .. automodule:: axelrod.strategies.grudger
    :members:
    :undoc-members:
@@ -120,6 +123,6 @@ Here are the docstrings of all the strategies in the library.
 .. automodule:: axelrod.strategies.titfortat
    :members:
    :undoc-members:
-.. automodule:: axelrod.strategies.gradualkiller
+.. automodule:: axelrod.strategies.worseandworse
    :members:
-   :undoc-members:
+   :undoc-members:

docs/reference/bibliography.rst

@@ -18,4 +18,4 @@ documentation.
 .. [Stewart2012] Stewart, a. J., & Plotkin, J. B. (2012). Extortion and cooperation in the Prisoner’s Dilemma. Proceedings of the National Academy of Sciences, 109(26), 10134–10135. http://doi.org/10.1073/pnas.1208087109
 .. [Szabó1992] Szabó, G., & Fáth, G. (2007). Evolutionary games on graphs. Physics Reports, 446(4-6), 97–216. http://doi.org/10.1016/j.physrep.2007.04.004
 .. [Tzafestas2000] Tzafestas, E. (2000). Toward adaptive cooperative behavior. From Animals to Animals: Proceedings of the 6th International Conference on the Simulation of Adaptive Behavior {(SAB-2000)}, 2, 334–340.
-.. [PRISON1998] LIFL (1998) PRISON. Available at: http://www.lifl.fr/IPD/ipd.frame.html (Accessed: 19 September 2016).
+.. [PRISON1998] LIFL (1998) PRISON. Available at: http://www.lifl.fr/IPD/ipd.frame.html (Accessed: 19 September 2016).

docs/tutorials/advanced/classification_of_strategies.rst

@@ -47,7 +47,7 @@ strategies::
     ... }
     >>> strategies = axl.filtered_strategies(filterset)
     >>> len(strategies)
-    35
+    36
 
 
 Or, to find out how many strategies only use 1 turn worth of memory to
@@ -81,7 +81,7 @@ length of each match of the tournament::
     ... }
     >>> strategies = axl.filtered_strategies(filterset)
     >>> len(strategies)
-    4
+    5
 
 Note that in the filterset dictionary, the value for the 'makes_use_of' key
 must be a list. Here is how we might identify the number of strategies that use